import numpy as np
import os
import os.path


def loadDataSet():
    postingList = [['my', 'dog', 'has', 'flea', 'problems', 'help', 'please'],
                   ['maybe', 'not', 'take', 'him', 'to', 'dog', 'park', 'stupid'],
                   ['my', 'dalmation', 'is', 'so', 'cute', 'I', 'love', 'him'],
                   ['stop', 'posting', 'stupid', 'worthless', 'garbage'],
                   ['mr', 'licks', 'ate', 'my', 'steak', 'how', 'to', 'stop', 'hime'],
                   ['quit', 'buying', 'worthless', 'dog', 'food', 'stupid']]
    classVec = [0, 1, 0, 1, 0, 1]
    return postingList, classVec


def createVocabList(dataSet):
    vocabSet = set([])
    for document in dataSet:
        vocabSet = vocabSet | set(document)
    return list(vocabSet)


def setOfWords2Vec(vocabList, inputSet):
    returnVec = [0] * len(vocabList)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] = 1
        else:
            print("the word: %s is not in my Vocabulary!" % word)
    return returnVec


def bagOfWords2VecMN(vocabList, inputSet):
    returnVec = [0] * len(vocabList)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)] += 1

    return returnVec

def trainNB0(trainMatrix, trainCategory):
    numTrainDocs = len(trainMatrix)
    numWords = len(trainMatrix[0])
    pAbusive = sum(trainCategory) / float(numTrainDocs)
    p0Num = np.ones(numWords);
    p1Num = np.ones(numWords)
    p0Denom = 2.0;
    p1Denom = 2.0;
    for i in range(numTrainDocs):
        if trainCategory[i] == 1:
            p1Num += trainMatrix[i];
            p1Denom += sum(trainMatrix[i])
        else:
            p0Num += trainMatrix[i]
            p0Denom += sum(trainMatrix[i])
    p1Vect = np.log(p1Num / p1Denom)
    p0Vect = np.log(p0Num / p0Denom)

    return p0Vect, p1Vect, pAbusive


def classifyNB(vec2Classify, p0Vec, p1Vec, pClass1):
    p1 = sum(vec2Classify * p1Vec) + np.log(pClass1)
    p0 = sum(vec2Classify * p0Vec) + np.log(1.0 - pClass1)
    if (p1 > p0):
        return 1
    else:
        return 0


def testingNB():
    listOPosts,listClasses = loadDataSet()
    myVocabList = createVocabList(listOPosts)
    trainMat = []
    for postinDoc in listOPosts:
        trainMat.append(setOfWords2Vec(myVocabList, postinDoc))
    p0V,p1V,pAb = trainNB0(np.array(trainMat), np.array(listClasses))
    testEntry = ['love', 'my', 'dalmation']
    thisDoc = np.array(setOfWords2Vec(myVocabList, testEntry))
    print(testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb))
    testEntry = ['stupid', 'garbage']
    thisDoc = np.array(setOfWords2Vec(myVocabList, testEntry))
    print(testEntry, 'classified as: ', classifyNB(thisDoc, p0V, p1V, pAb))


def textParse(bigString):
    import re
    listOfTokens = re.split(r'\W*', bigString)
    return [tok.lower() for tok in listOfTokens if len(tok) > 2]

def spamTest():
    docList = []
    classList = []
    fullText = []
    for i in range(1, 26):
        wordList = textParse(open('email/spam/%d.txt' % i, encoding="latin-1").read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(1)
        wordList = textParse(open('email/ham/%d.txt' % i, encoding="latin-1").read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(0)

    vocabList = createVocabList(docList)
    trainingSet = list(range(50))
    testSet = []
    for i in range(10):
        randIndex = int(np.random.uniform(0, len(trainingSet)))
        testSet.append(trainingSet[randIndex])
        del(trainingSet[randIndex])

    trainMat = []
    trainClass = []
    for docIndex in trainingSet:
        trainMat.append(setOfWords2Vec(vocabList, docList[docIndex]))
        trainClass.append(classList[docIndex])
    p0V, p1V, pSpam = trainNB0(np.array(trainMat), np.array(trainClass))
    errorCount = 0
    for docIndex in testSet:
        wordVector = setOfWords2Vec(vocabList, docList[docIndex])
        if classifyNB(np.array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:
            errorCount += 1
            print("classification error", docList[docIndex])
    print('the error rate is: ', float(errorCount) / len(testSet))


def calcMostFreq(vocabList, fullText, num):
    import operator;
    freqDict = {}
    for token in vocabList:
        freqDict[token] = fullText.count(token)
    sortedFreq = sorted(freqDict.items(), key=operator.itemgetter(1), reverse=True)
    return sortedFreq[:num]


def localWords():
    docList = []
    classList = []
    fullText = []
    spams = len([name for name in os.listdir('email/spam') if os.path.isfile(os.path.join('email/spam', name))])
    hams = len([name for name in os.listdir('email/ham') if os.path.isfile(os.path.join('email/ham', name))])

    minLen = min(spams, hams)
    for i in range(1, minLen + 1):
        wordList = textParse(open('email/spam/%d.txt' % i, encoding="latin-1").read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(1)
        wordList = textParse(open('email/ham/%d.txt' % i, encoding="latin-1").read())
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(0)
    vocabList = createVocabList(docList)

    top10Words = calcMostFreq(vocabList, fullText, 10)
    for pairW in top10Words:
        if pairW[0] in vocabList:
            vocabList.remove(pairW[0])
    trainingSet = list(range(2 * minLen))
    testSet = []
    for i in range(10):
        randIndex = int(np.random.uniform(0, len(trainingSet)))
        testSet.append(trainingSet[randIndex])
        del(trainingSet[randIndex])
    trainMat = []
    trainClasses = []
    for docIndex in trainingSet:
        trainMat.append(bagOfWords2VecMN(vocabList, docList[docIndex]))
        trainClasses.append(classList[docIndex])
    p0V, p1V, pSpam = trainNB0(np.array(trainMat), np.array(trainClasses))
    errorCount = 0
    for docIndex in testSet:
        wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])
        if classifyNB(np.array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:
            errorCount += 1
    print('the error rate is: ', float(errorCount) / len(testSet))
    return vocabList, p0V, p1V


def getTopWords():
    import operator
    vocabList, p0V, p1V = localWords()
    topSpam = []
    topHam = []
    for i in range(len(p0V)):
        if p0V[i] > -6.0: topSpam.append((vocabList[i], p0V[i]))
        if p1V[i] > -6.0: topHam.append((vocabList[i], p1V[i]))
    sortedSpam = sorted(topSpam, key=lambda pair: pair[1], reverse=True)
    print("SPAM**SPAM**SPAM**SPAM**SPAM**SPAM**SPAM**SPAM**SPAM**SPAM**SPAM**SPAM**")
    for item in sortedSpam:
        print(item[0])
    sortedHam = sorted(topHam, key=lambda pair: pair[1], reverse=True)
    print("HAM**HAM**HAM**HAM**HAM**HAM**HAM**HAM**HAM**HAM**HAM**HAM**")
    for item in sortedHam:
        print(item[0])